In industries such as telecommunications, the monitoring of the performance and delivered quality of a telecommunications network by the owner of the network is a very complex task. To assist in this monitoring, the specification, computation and navigation of Key Performance Indicators (KPIs) and Key Quality Indicators (KQIs) in a telecommunications Service Quality Management (SQM) and Service Level Management (SLM) environment can be used. SQM and SLM systems today typically have the following functions and components, service models, Service Level Agreement (SLA) models and instances, metadata, a computational engine and KQI monitoring.
The service models can include service elements, Key Quality Indicator (KQI) models, Key Performance Indicator (KPI) models and root cause analysis reports. The Service Level Agreement (SLA) models and instances can include SLA clauses, SLA schedules and SLA reports, etc. The metadata for adapters can be used to collect appropriate data from different Operational Support Systems (OSS) including metrics, resource dimensions, database storage and mediation. The computational engine can compute complex KPIs and KQIs and combined KQIs based on a (single) resource dimension, evaluate KQI values against a defined threshold and execute actions on SLA violations (e.g. escalate via email, SNMP, etc.). The KQI monitoring (usually supported by a Root Cause Analysis process) requires a user to drill-down hierarchically on a single dimension usually a via report, view a metric but computed on different dimensions, and view related KQIs on different dimensions.
FIG. 1 illustrates a functional block diagram of a Service Quality Management/Service Level Management system. With the fixed to mobile convergence that is occurring in the telecommunications industry today, as well as the fact that more and more services are being created almost daily, the cardinality of the KQI models and resource dimensions that SQM/SLM systems will have to manage will increase by an order of magnitude. Coupled to the telecommunication network growth is an increasing need from the SQM/SLM platform end-users perspective to be able to slice and dice the received metric information according to multiple dimensions. Such dimensions would typically be the customer (or groups of customers), the type of device being used to access the network (mobile phone, laptop etc) but also geographical and topological dimensions. This new need to compute and present the service quality information according to this ever extending set of dimensions introduces fundamental scalability challenges in terms of hardware requirements to be able to cope with the load introduced by these growing networks.
This increase in KQI models and resource dimensions will require an equivalent increase in processing power and memory to manage them. Current systems- must produce new KPI/KQI models for each metric/dimension combination. This leads to a requirement for a Cartesian product of KQI models which in turn increases the storage, memory, and processor requirements to process the additional models. Root cause analysis and the method of drilling down hierarchically on a dimension (e.g. geographical location and/or across dimensions of the same metric and across to different but related metrics is currently implemented via custom reporting or custom software. This mechanism becomes increasingly labour-intensive in an environment with ever increasing number of services and data sources that need to be supported in a single system. This increases the total cost of ownership of such products.